Real-time awareness driven by real-time data. Last updated : 2025-10-14

TEXT FILLER We combine state-of-the-art data resources and computer modelling to provide rapid, real-time insights into the developing dengue season across the world.

By comparing the current season to the past seasons, we provide a seasonal severity score. Higher scores indicate that the current season is worse than most of the previous seasons, while lower scores indicate that the current season is better than most of the previous seasons.

High Severity Countries

Argentina
Plot for Argentina
Aruba
Plot for Aruba
Australia
Plot for Australia
Bangladesh
Plot for Bangladesh
Barbados
Plot for Barbados

TEXT FILLER — By comparing the current season to the past seasons, we provide a seasonal severity score. Higher scores indicate that the current season is worse than most of the previous seasons, while lower scores indicate that the current season is better than most of the previous seasons.

Argentina
Plot for Argentina
Aruba
Plot for Aruba
Australia
Plot for Australia
Bangladesh
Plot for Bangladesh
Barbados
Plot for Barbados
Belize
Plot for Belize
Bhutan
Plot for Bhutan
Bolivia
Plot for Bolivia
Brazil
Plot for Brazil
Burkina Faso
Plot for Burkina Faso
Cambodia
Plot for Cambodia
Cayman Islands
Plot for Cayman Islands
China
Plot for China
Colombia
Plot for Colombia
Cook Islands
Plot for Cook Islands
Costa Rica
Plot for Costa Rica
Cuba
Plot for Cuba
Dominican Republic
Plot for Dominican Republic
Ecuador
Plot for Ecuador
El Salvador
Plot for El Salvador
Eritrea
Plot for Eritrea
Fiji
Plot for Fiji
French Guiana
Plot for French Guiana
French Polynesia
Plot for French Polynesia
Grenada
Plot for Grenada
Guadeloupe
Plot for Guadeloupe
Guatemala
Plot for Guatemala
Guyana
Plot for Guyana
Honduras
Plot for Honduras
India
Plot for India
Indonesia
Plot for Indonesia
Jamaica
Plot for Jamaica
Kenya
Plot for Kenya
Kiribati
Plot for Kiribati
Lao People's Democratic Republic
Plot for Lao People's Democratic Republic
Malaysia
Plot for Malaysia
Maldives
Plot for Maldives
Marshall Islands
Plot for Marshall Islands
Martinique
Plot for Martinique
Mexico
Plot for Mexico
Myanmar
Plot for Myanmar
Nepal
Plot for Nepal
New Caledonia
Plot for New Caledonia
Nicaragua
Plot for Nicaragua
Pakistan
Plot for Pakistan
Palau
Plot for Palau
Panama
Plot for Panama
Paraguay
Plot for Paraguay
Peru
Plot for Peru
Puerto Rico
Plot for Puerto Rico
Saint Barthelemy
Plot for Saint Barthelemy
Saint Lucia
Plot for Saint Lucia
Samoa
Plot for Samoa
Senegal
Plot for Senegal
Singapore
Plot for Singapore
Sint Maarten
Plot for Sint Maarten
Solomon Islands
Plot for Solomon Islands
Sri Lanka
Plot for Sri Lanka
Sudan
Plot for Sudan
Suriname
Plot for Suriname
Thailand
Plot for Thailand
Timor-Leste
Plot for Timor-Leste
Tonga
Plot for Tonga
Tuvalu
Plot for Tuvalu
Vanuatu
Plot for Vanuatu
Viet Nam
Plot for Viet Nam
Myanmar
Plot for Myanmar
Marshall Islands
Plot for Marshall Islands
Australia
Plot for Australia
French Polynesia
Plot for French Polynesia
Nicaragua
Plot for Nicaragua
Indonesia
Plot for Indonesia
Eritrea
Plot for Eritrea
Brazil
Plot for Brazil
El Salvador
Plot for El Salvador
Kenya
Plot for Kenya
Colombia
Plot for Colombia
Tuvalu
Plot for Tuvalu
Cuba
Plot for Cuba
Thailand
Plot for Thailand
Mexico
Plot for Mexico
Malaysia
Plot for Malaysia
Puerto Rico
Plot for Puerto Rico
Viet Nam
Plot for Viet Nam
Kiribati
Plot for Kiribati
Sri Lanka
Plot for Sri Lanka
India
Plot for India
Bolivia
Plot for Bolivia
Dominican Republic
Plot for Dominican Republic
Costa Rica
Plot for Costa Rica
Ecuador
Plot for Ecuador
Singapore
Plot for Singapore
Cambodia
Plot for Cambodia
Barbados
Plot for Barbados
Honduras
Plot for Honduras
Paraguay
Plot for Paraguay
Aruba
Plot for Aruba
Panama
Plot for Panama
Guyana
Plot for Guyana
Lao People's Democratic Republic
Plot for Lao People's Democratic Republic
Guatemala
Plot for Guatemala
Sudan
Plot for Sudan
Belize
Plot for Belize
Maldives
Plot for Maldives
Jamaica
Plot for Jamaica
Peru
Plot for Peru
Pakistan
Plot for Pakistan
Bangladesh
Plot for Bangladesh
Suriname
Plot for Suriname
Sint Maarten
Plot for Sint Maarten
Burkina Faso
Plot for Burkina Faso
Fiji
Plot for Fiji
Cayman Islands
Plot for Cayman Islands
China
Plot for China
Saint Lucia
Plot for Saint Lucia
Bhutan
Plot for Bhutan
Nepal
Plot for Nepal
Senegal
Plot for Senegal
Palau
Plot for Palau
Guadeloupe
Plot for Guadeloupe
Grenada
Plot for Grenada
Solomon Islands
Plot for Solomon Islands
French Guiana
Plot for French Guiana
Cook Islands
Plot for Cook Islands
Saint Barthelemy
Plot for Saint Barthelemy
Samoa
Plot for Samoa
Tonga
Plot for Tonga
Martinique
Plot for Martinique
Argentina
Plot for Argentina
Timor-Leste
Plot for Timor-Leste
New Caledonia
Plot for New Caledonia
Vanuatu
Plot for Vanuatu

South America

Argentina
Plot for Argentina
Bolivia
Plot for Bolivia
Brazil
Plot for Brazil
Colombia
Plot for Colombia
Ecuador
Plot for Ecuador
French Guiana
Plot for French Guiana
Guyana
Plot for Guyana
Paraguay
Plot for Paraguay
Peru
Plot for Peru
Suriname
Plot for Suriname

Caribbean

Aruba
Plot for Aruba
Barbados
Plot for Barbados
Cayman Islands
Plot for Cayman Islands
Cuba
Plot for Cuba
Dominican Republic
Plot for Dominican Republic
Grenada
Plot for Grenada
Guadeloupe
Plot for Guadeloupe
Jamaica
Plot for Jamaica
Martinique
Plot for Martinique
Puerto Rico
Plot for Puerto Rico
Saint Barthelemy
Plot for Saint Barthelemy
Saint Lucia
Plot for Saint Lucia
Sint Maarten
Plot for Sint Maarten

Pacific Islands

Australia
Plot for Australia
Cook Islands
Plot for Cook Islands
Fiji
Plot for Fiji
French Polynesia
Plot for French Polynesia
Kiribati
Plot for Kiribati
Marshall Islands
Plot for Marshall Islands
New Caledonia
Plot for New Caledonia
Palau
Plot for Palau
Samoa
Plot for Samoa
Solomon Islands
Plot for Solomon Islands
Tonga
Plot for Tonga
Tuvalu
Plot for Tuvalu
Vanuatu
Plot for Vanuatu

South Asia

Bangladesh
Plot for Bangladesh
Bhutan
Plot for Bhutan
India
Plot for India
Maldives
Plot for Maldives
Nepal
Plot for Nepal
Pakistan
Plot for Pakistan
Sri Lanka
Plot for Sri Lanka

Central America & Mexico

Belize
Plot for Belize
Costa Rica
Plot for Costa Rica
El Salvador
Plot for El Salvador
Guatemala
Plot for Guatemala
Honduras
Plot for Honduras
Mexico
Plot for Mexico
Nicaragua
Plot for Nicaragua
Panama
Plot for Panama

Sub-Saharan Africa

Burkina Faso
Plot for Burkina Faso
Eritrea
Plot for Eritrea
Kenya
Plot for Kenya
Senegal
Plot for Senegal

East & Southeast Asia

Cambodia
Plot for Cambodia
China
Plot for China
Indonesia
Plot for Indonesia
Lao People's Democratic Republic
Plot for Lao People's Democratic Republic
Malaysia
Plot for Malaysia
Myanmar
Plot for Myanmar
Singapore
Plot for Singapore
Thailand
Plot for Thailand
Timor-Leste
Plot for Timor-Leste
Viet Nam
Plot for Viet Nam

Europe, Middle East & North Africa

Sudan
Plot for Sudan
Collection of Real-Time Data

-WHO/ PAHO / SEARO crawlers - about, [link] (more…)

Historical data and Averages OpenDENGUE [link]
Estimating the reporting factor of American Nations

For American nations, the estimates of reporting factors were calculated empirically from the PAHO DENV cases dashboard PLISA Health Information Platform for the Americas. From June 2022 to February 2023 the PAHO DENV cases dashboard was downloaded weekly. Additionally the DENV case data for the same time period was downloaded from the PAHO DENV cases dashboard in July 2024. Using this data the reporting factor for each country at each lag in data reporting could be estimated using the following equation:

\[\mathbf{f}_{c,d} = (\frac{1}{T} \sum_{t=i}^{T} \left( \frac{N_{t,c,d}}{V_{t,c}} \right))/1\] where N is the number of DENV cases for a given country (c), at a given epiweek (t) for a given delay (d), V is the validated count of DENV cases for a given country (c), at a given epiweek (t) and f is the reporting factor for a given country (c), at a given epiweek (t) for a given delay (d). T is the number of observation recorded.

Correcting DENV cases

For all nations the most recent update was considered to be the most accurate data for the monthly counts of DENV cases. For PAHO nations this case count was multiplied by the average reporting factor at corresponding delay and country. Four nations (Belize, Dominica, Barbados and Paraguay) did not undergo this correct due to the complex nature of reporting factors in those countries. This corrections resulted in small differences in the monthly and overall cause count.

Forecasting using average monthly proportions

Understanding this underlying seasonal profile can provide a basis for predicting future case load, using observations of the number of cases observed to date within a given season. Dividing cumulative cases observed within a given season by the expected cumulative proportion returns an estimate of the total number of cases expected for that season. Multiplying this figure by expected monthly proportion returns an estimate of monthly cases. Fig. 2 uses data from Thailand and Fiji, locations with 32 and 3 seasons of data respectively, to demonstrate this use case. Here existing seasonal profiles are used to predict the most recent season with data available. Predicted and observed values are closer together for Thailand than Fiji, highlighting the increased predictive power provided by increasing the number of seasons used for prediction.

Identifying dengue season

Using combined data from the OpenDengue and WHO dengue observatories we identify the calendar month with the lowest case load on average. We define this month as the first month of the dengue season. By example, if April is the calendar month with lowest case load on average, it becomes the first month of the season, and March the last month. This process normalises the seasonal profiles across different locations by setting a unified starting point. It also addresses instances where the peak season lies across the new year. Were analysis performed by calendar year, small changes in the timing of the season peak could lead to significant changes in the number of cases allocated to each year. Aligning data from calendar year to season accounts for such potential peak timing heterogeneity across years, which could skew results.

Once data has been aligned from calendar year to dengue season any locations with less than three seasons of data were removed. Monthly proportion of cases observed within each season was then calculated, normalising for between-season differences in total case load. Taking the average of this proportion across all seasons provides a baseline seasonal profile for each country. Standard deviation of this proportion provides a measure of uncertainty around this average proportion, accounting for between year differences in case load distribution. Figure 1 shows the average cumulative monthly proportion by country, with error bars representing the 95% confidence intervals.

CARD TEXT

based on cumulative data for the most recent month.

severity is defined by comparing the current season to the historical average season.

FILLER meta data:

  • Country
  • Region
  • source
  • Year
  • Month
  • season_nMonth
  • cases cum_todate_cases_calendar

  • Predicted_total_seasonal_cases
  • Ave_season_monthly_cases
  • Ave_monthly_proportion
  • Ave_season_monthly_cum_cases
  • Ave_cum_monthly_proportion

We plan to establish a climate-driven real-time global outbreak forecasting system for dengue by combining databases, modelling approaches, and levering the expertise of the dengue modelling and mapping group at LSHTM.

Establishing the observatory will require creating a new real-time database of dengue case data for over 50 countries to ensure we always have up-to-date data, developing flexible Bayesian forecasting models to make nowcasts and forecasts of outbreaks up to three months ahead and effectively communicating outbreak warnings at the national and regional level to trigger additional mosquito control to prevent epidemics.

Extension of our predictions to non-endemic areas in Europe and North America will localise the increasing threat climate change poses to mosquito-transmitted disease risk in these areas. Collaboration with individuals at the World Health Organization will extent the observatory’s reach and allow outbreak forecasts to reach the country decision makers necessary to act early and save lives.

The project has three main objectives:

  • Objective 1: Developing a global dengue database
  • Objective 2: Training and testing nowcast and forecast models
  • Objective 3: Issuing alerts and evaluating responses
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